The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry. 9 Mar This book develops the use of Monte Carlo methods in finance and it in financial engineering, researchers in Monte Carlo simulation, and. Compre o livro Monte Carlo Methods in Financial Engineering: 53 na Amazon. : confira as ofertas para livros em inglês e por Paul Glasserman (Autor).
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Monte Carlo Methods in Financial Engineering – Paul Glasserman – Google Books
Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering.
This approach considers a parametric class of exercise regions or stopping rules. This book is an excellent reference for any practitioner or academic alike highly recommended. The next part describes techniques for improving simulation accuracy and efficiency. Due to constraints of space, only the last two chapters will be reviewed here. The author’s discussion is somewhat too brief, but he does quote many references that the reader can easily consult.
Most interesting in the discussion is the use of heavy-tailed probability distributions to model the changes in market prices and risks.
Similar to this method are stochastic mesh methods, the difference being that stochastic mesh methods utilize information coming from all nodes in the next time step. This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. Still another method that is discussed in this chapter is that of state-space partitioning, which, as the name implies, involves the partitioning of the state space of the underlying Markov chain.
HendersonBarry L. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers [ One then must find the distribution of a quadratic function of normal random variable, which the author does numerically via transform inversion. The successful reader has a working knowledge of basic calculus, linear algebra, and probability. The author first treats the case where the risk factors are distributed according to multivariate normal distribution, and then latter the case where the distribution is heavy-tailed.
The Term Structure of Interest Rates The chapter on “Generating Random Numbers” helps, even if the description of the basic uniform generators could be stronger.
The main item of interest here is the calculation of the time of default, which the author discusses in terms of the default intensity and intensity-based modeling using a stochastic intensity to model the time to default.
Monte Carlo Methods in Financial Engineering. The most important prerequisite is familiarity with the mathematical tools used to methkds and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus.
Prior exposure to the basic principles of option pricing is useful but not essential. Compartilhe seus pensamentos com outros clientes. The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. This book gives a good overview of how they are used in financial engineering, with particular emphasis on pricing American options and risk management.
Softcover reprint of hardcover 1st ed. This nonlinearity arises because of the dependence of the option on the price of the underlying asset. I also felt a little pain at having no background in stochastic calculus, but some determination and a willingness to skip over fine points got me through well enough. Generating Random Numbers and Random Variables. The final third of the book addresses special topics: My library Help Advanced Book Search.
Prior exposure to the basic principles of option pricing is useful but not essential. This book is not. Seja o primeiro a avaliar este item.
The author reminds the reader of the pitfalls in using probability distributions based on historical data for sampling price changes. The last chapter will be of particular interest to risk managers, wherein the author applies Monte Carlo simulation to portfolio management. The “Sample Path” glassfrman is where I came into this book, really, looking for more insight into generation Brownian bridges.
Given the uniform generator, its descriptions of generators for non-uniform distributions work well for me. The author discusses the problems with this approach, these arising mostly financiap high-dimensional state spaces, as expected.
The final third of the book addresses special topics: The author also discusses various methods for doing variance reduction in the heavy-tailed case, one of these methods again involving exponential twisting. Monte Carlo Methods in Financial Engineering. The next few chapters on variance reduction, ccarlo, discretization, and sensitivity analysis are all widely applicable financia, I don’t have immediate use for the material, but now I know where to look when the need arises.
This book develops the use of Monte Carlo methods in engineeeing Applications in Risk Engineerin The author gives references, and discuses in slight detail, results that show the asymptotic optimality for this method. Aspiring financial engineers will find much that is helpful in the book, and after reading it should be able to apply the methodologies in the book in whatever financial institution they find themselves employed. That reader must have a real interest in MC techniques, and should care about the financial decision-making to which Glasserman applies those methocs – but, as I prove, even that isn’t necessary for getting a lot of value from this text.
This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering.
It divides roughly into three parts. Leia mais Leia menos. Monte Carlo simulations are extensively glawserman not only in finance but also in network modeling, bioinformatics, radiation therapy planning, physics, and meteorology, to name a few. The author discusses briefly the numerical tests that support this method.
Fale com a Editora! Handbooks in Operations Research and Management Science: The case for a heavy-tailed distribution if of course much more involved, since there are no moment generating functions for the quantities of interest.